The Credibility Revolution in Empirical Economics: How Better Research Design is Taking the Con Out of Econometrics

By Josh Angrist, Jörn-Steffen Pischke. Social Science Research Network, 2010

1,567 citations recorded by the source corpus.

This essay reviews progress in empirical economics since Leamer’s (1983) critique. Leamer highlighted the benefits of sensitivity analysis, a procedure in which researchers show how their results change with changes in specification or functional form. Sensitivity analysis has had a salutary but not a revolutionary effect on econometric practice. As we see it, the credibility revolution in empirical work can be traced to the rise of a design-based approach that emphasizes the identification of causal effects. Design-based studies typically feature either real or natural experiments and are distinguished by their prima facie credibility and by the attention investigators devote to making the case for a causal interpretation of the findings their designs generate. Design-based studies are most often found in the microeconomic fields of Development, Education, Environment, Labor, Health, and Public Finance, but are still rare in Industrial Organization and Macroeconomics. We explain why IO and Macro would do well to embrace a design-based approach. Finally, we respond to the charge that the design-based revolution has overreached.

Subfields: Economics and Econometrics, Education, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/5206461b87131e851f21eca0243a480d98a34185

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Citations in the corpus

Estimation of Random-Coefficient Demand Models: Two Empiricists' Perspective (2014, neutral): “The reasons for such divergence have been discussed in a rather lively manner in Angrist and Pischke (2010) and Nevo and Whinston (2010). At the same time, a prominent class of such models, the demand models for differentiated products of the Berry et al.”
From the Lab to the Field: A Review of Tax Experiments (2016, methodology): “The first one has to do with methods and to the ‘credibility revolution’ that applied economics has been experiencing in recent years (Angrist and Pischke, 2010). Field experiments combine rigorous methods with high policy relevance better than other methods in the tax compliance literature. For thi”
Thinking Clearly About Correlations and Causation: Graphical Causal Models for Observational Data (2017, background): “As Angrist and Pischke (2010) noted, “In the empirical universe, evidence accumulates across settings and study designs, ultimately producing some kind of consensus” (p. 25).”
Endogeneity: A Review and Agenda for the Methodology-Practice Divide Affecting Micro and Macro Research (2020, background): “Angrist & Krueger, 1999; Angrist & Pischke, 2010 Measurement Error Model Measurement Error –”
Measuring Substitution Patterns in Diﬀerentiated-Products Industries (2019, neutral): “Thus policy conclusions drawn from the model cannot be directly linked to moments in the data that are driving those conclusions (see e.g., Angrist and Pischke (2010)).”
Unobservable Selection and Coefficient Stability: Theory and Evidence (2019, background): “Leamer (1978) and Angrist and Pischke (2010) (among others) provided more discussion on the related topic of model specification in search of a causal effect.”
What You Can and Can’t Properly Do with Regression (2010, background): “The fourth response is to rely on research designs far better suited for causal inference than the usual observational approaches (Angrist and Pischke 2010; Campbell and Stanley 1963; Imbens 2004).”